Description Usage Arguments Details Value Author(s) References Examples
Fits a minimal forest to data and visualizes it.
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data |
A normalized dataframe or matrix with no missing data of continuous and (or) categorical measurements. |
stat |
Measure to be minimized: LR, AIC, or BIC (the default). The default is BIC. It can also be a user-defined function with the format: FUN (model, dataset, previous, forbEdges); where the parameters are defined as in chStat. The function must return a structure as in chStat. |
community |
A logical value to show if the node communities should be detected and colored in the returned graph. (default = TRUE) |
betweenness |
A logical value to show if the node betweenness measurements should be computed and returned from the function. (default = TRUE) |
plot |
A logical value to show if the graph should be plotted. (default = FALSE) |
levels |
An integer value indicating the maximum number of levels of a categorical variable. To be used to distinguish the categorical variable.
Defaults to NULL because it is supposed that |
The function is a wrapper for bnlearn package implementing several algorithms including Constraint-based algorithms (i.e., Max-Min Parents and Children, Semi-Interleaved HITON-PC, and Grow-Shrink), Score-based algorithms (i.e., Hill-Climbing and Tabu Search), and Hybrid algorithms (i.e., Max-Min Hill-Climbing), and Local Discovery algorithms (i.e, Max-Min Parents and Children and ARACNE). If one uses a more than one algorithm, the function combines all of the algorithms and returns a graph based on the combination. The graph is constructed based on the strength of associations calculated by bootstrapping.
a list containing:
significanse |
A data.frame containing edges with p-statistics and p.values. |
summary |
a gRapHD object which is the fit model. |
graph |
an igraph object. |
betweenness |
betweenness measurements of each node. |
network |
a visNetwork plot of the graph. |
communities |
a named vector indicating the community of each node. |
Elyas Heidari
Chow, C.K., and Liu, C.N. (1968) Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory, Vol. IT-14, 3:462-7.
Edwards, D., de Abreu, G.C.G. and Labouriau, R. (2010). Selecting high- dimensional mixed graphical models using minimal AIC or BIC forests. BMC Bioinformatics, 11:18.
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